TD‑Grokking lets LLMs crack zero‑reward RL tasks
TD‑Grokking introduces training‑time decomposition, breaking impossible zero‑reward problems into verifiable subproblems that yield non‑zero rewards. This transforms sparse‑signal RLVR into a usable training signal, boosting LLM performance on challenging math and medical benchmarks beyond vanilla GRPO and other baselines.